The Backbone of Decision Support Systems: The Sensor to Decision Chain

The Backbone of Decision Support Systems: The Sensor to Decision Chain

Philipp Hertweck (Fraunhofer IOSB, Institute of Optronics, System Technologies and Image Exploitation, Karlsruhe, Germany), Jürgen Moßgraber (Fraunhofer IOSB, Institute of Optronics, System Technologies and Image Exploitation, Karlsruhe, Germany), Efstratios Kontopoulos (Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece), Panagiotis Mitzias (Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece), Tobias Hellmund (Fraunhofer IOSB, Institute of Optronics, System Technologies and Image Exploitation, Karlsruhe, Germany), Anastasios Karakostas (Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece), Désirée Hilbring (Fraunhofer IOSB, Institute of Optronics, System Technologies and Image Exploitation, Karlsruhe, Germany), Hylke van der Schaaf (Fraunhofer IOSB, Institute of Optronics, System Technologies and Image Exploitation, Karlsruhe, Germany), Stefanos Vrochidis (Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece), Jan-Wilhem Blume (Fraunhofer IOSB, Institute of Optronics, System Technologies and Image Exploitation, Karlsruhe, Germany) and Ioannis Kompatsiaris (Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece)
Copyright: © 2021 |Pages: 23
DOI: 10.4018/978-1-7998-9023-2.ch002
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Abstract

Understanding the current situation is critical in every natural disaster or crisis. Therefore, there is a need for accurate and up-to-date information about the scope, extent and impact of a disaster. The basis for this information is data that is available through a variety of sensors. Decision Support Systems (DSSs) support decision makers in disaster management, response, and recovery by providing early warnings, insights into the current situation and recommendations for mitigation actions. For this purpose, raw sensor data needs to be collected, analyzed, integrated, and its semantics need to be automatically understood by the system. This series of processes forms a generic sensor to decision chain. In this paper, we present solutions and technologies to integrate those steps seamlessly, also demonstrating how each step of the pipeline can be visualized.
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Introduction

The World Meteorological Organization expects a global temperature increase of 3°C caused by climate change (World Meteorological Organization, 2018). This increases the probability for the occurrence of critical natural events, such as floods, dry periods (resulting in forest fires) or heatwaves. To face these challenges, the United Nations have called to intensify the development of early warning systems (UNISDR, 2005). In 2015, the call was renewed, now also including chained disasters (UNISDR, 2015). Through the support of early warning and risk management systems, authorities aim to limit the impact of natural disaster crises.

In this paper, we propose a generic approach to improve the quality of decision support and foster early warning systems. Our method, called the “sensor to decision chain,” covers the steps from sensor data acquisition1, semantic data analysis, data integration and eventual decision support. The chain forms the basis of an integration framework supported by a variety of cutting-edge technologies. Therefore, the main goal of the framework is to support authorities through a decision support system that implements the sensor to decision chain.

The following subsections describe each step of the sensor to decision chain and present appropriate technologies for its implementation. The rest of this paper is structured as follows: Section “Background” discusses existing decision support workflows and respective implementations; “Motivation” presents the beAWARE project, which serves as a practical example of our implementation and offers the possibility to test our approach in three large-scale pilots; the general approach is discussed in “The sensor to decision chain” section, followed by an thorough description of each step. Finally, “Conclusion” summarizes our findings and discusses directions for further research.

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